AI Vital For Rapid Identification of Drugs That Can Be Repurposed To Combat COVID-19, Says New Report
|
By HospiMedica International staff writers Posted on 21 Aug 2020 |

Illustration
Artificial intelligence (AI) offers significant promise for biopharmaceutical companies to speed up the drug repurposing process for finding new therapies to treat COVID-19, according to a new report.
The COVID-19 Executive Briefing report by GlobalData (London, UK; www.globaldata.com), a research, consulting and events firm, states that drug repurposing is among the fastest and safest methods to seek new therapies for the treatment of COVID-19, as the drugs are already being used for treating various existing conditions, thus reducing the possibility of adverse reactions. AI technologies are expected to play a significant role in allowing biopharmaceutical companies to shorten the time required for pre-clinical drug identification and design process.
The Innovation Explorer database of GlobalData’s Disruptor Intelligence Center has revealed how AI startups are helping identify suitable molecules that target COVID-19. For instance, BenevolentAI, a startup, is using its AI-based drug discovery platform to identify drugs that have the potential to disrupt selected viral entry pathways of COVID-19, thereby preventing the virus from replicating. Another startup Elix has harnessed various neural networks in order to predict the chemical properties of molecules with the capacity to neutralize COVID-19. Similarly, Gero, a startup, has leveraged AI to rapidly screen existing drug molecules for the treatment of COVID-19. Additionally, other startups such as Repurpose.AI and Atomwise are entering into partnerships with global research institutes to utilize their AI-powered predictive models for locating new drug molecules for the treatment of COVID-19.
“Typically developing a new drug takes almost a decade and costs anywhere between $2 billion to USD 3 billion,” said Venkata Naveen, Senior Disruptive Tech Analyst at GlobalData. “But now biopharmaceutical companies are in dire need to accelerate the entire drug development process given that COVID-19 cases and deaths are mounting every day. Under these circumstances, AI technologies allow companies to significantly shorten the pre-clinical drug identification and design process from several years to a few days or months.”
Related Links:
GlobalData
The COVID-19 Executive Briefing report by GlobalData (London, UK; www.globaldata.com), a research, consulting and events firm, states that drug repurposing is among the fastest and safest methods to seek new therapies for the treatment of COVID-19, as the drugs are already being used for treating various existing conditions, thus reducing the possibility of adverse reactions. AI technologies are expected to play a significant role in allowing biopharmaceutical companies to shorten the time required for pre-clinical drug identification and design process.
The Innovation Explorer database of GlobalData’s Disruptor Intelligence Center has revealed how AI startups are helping identify suitable molecules that target COVID-19. For instance, BenevolentAI, a startup, is using its AI-based drug discovery platform to identify drugs that have the potential to disrupt selected viral entry pathways of COVID-19, thereby preventing the virus from replicating. Another startup Elix has harnessed various neural networks in order to predict the chemical properties of molecules with the capacity to neutralize COVID-19. Similarly, Gero, a startup, has leveraged AI to rapidly screen existing drug molecules for the treatment of COVID-19. Additionally, other startups such as Repurpose.AI and Atomwise are entering into partnerships with global research institutes to utilize their AI-powered predictive models for locating new drug molecules for the treatment of COVID-19.
“Typically developing a new drug takes almost a decade and costs anywhere between $2 billion to USD 3 billion,” said Venkata Naveen, Senior Disruptive Tech Analyst at GlobalData. “But now biopharmaceutical companies are in dire need to accelerate the entire drug development process given that COVID-19 cases and deaths are mounting every day. Under these circumstances, AI technologies allow companies to significantly shorten the pre-clinical drug identification and design process from several years to a few days or months.”
Related Links:
GlobalData
Latest AI News
- AI Analysis of Pericardial Fat Refines Long-Term Heart Disease Risk
- Machine Learning Approach Enhances Liver Cancer Risk Stratification
- New AI Approach Monitors Brain Health Using Passive Wearable Data
- AI Tool Maps Early Risk Patterns in Bloodstream Infections
- AI Model Identifies Rare Endocrine Disorder from Hand Images
- AI Tool Promises to Reduce Length of Hospital Stays and Free Up Beds
Channels
Artificial Intelligence
view channelAI Analysis of Pericardial Fat Refines Long-Term Heart Disease Risk
Accurately identifying long-term cardiovascular disease risk in asymptomatic adults remains challenging for clinicians. Missed or underestimated risk delays preventive therapy and increases the chance... Read more
Machine Learning Approach Enhances Liver Cancer Risk Stratification
Hepatocellular carcinoma, the most common form of primary liver cancer, is often detected late despite targeted surveillance programs. Current screening guidelines emphasize patients with known cirrhosis,... Read moreCritical Care
view channel
Angiography-Based FFR Approach Matches Gold Standard Results Without Wires
Accurately determining whether a coronary stenosis limits blood flow is essential to guide percutaneous coronary intervention, yet wire-based physiologic testing remains underused due to added procedural... Read more
Eye Imaging AI Identifies Elevated Cardiovascular Risk
Many adults at risk for atherosclerotic cardiovascular disease are not identified until they undergo formal primary care assessment. Delayed risk recognition can postpone initiation of statins and lifestyle... Read moreSurgical Techniques
view channel
Fiber-Form Bone Graft Expands Intraoperative Options for Spinal Fusion
Spinal and orthopedic fusion procedures often require bone graft materials that handle predictably and support bone formation. Surgeons face added complexity in difficult anatomy and challenging fusion environments.... Read more
Ultrasound‑Aided Catheter Treatment Cuts Early Collapse in Pulmonary Embolism
Acute pulmonary embolism can cause rapid hemodynamic deterioration and early death in hospitalized and emergency patients. Systemic thrombolysis can dissolve clots but is limited by a high risk of major... Read morePatient Care
view channel
Wearable Sleep Data Predict Adherence to Pulmonary Rehabilitation
Chronic obstructive pulmonary disease (COPD) is a long-term lung disorder that makes breathing difficult and often disturbs sleep, reducing energy for daily activities. Limited engagement in pulmonary... Read more
Revolutionary Automatic IV-Line Flushing Device to Enhance Infusion Care
More than 80% of in-hospital patients receive intravenous (IV) therapy. Every dose of IV medicine delivered in a small volume (<250 mL) infusion bag should be followed by subsequent flushing to ensure... Read moreHealth IT
view channel
Voice-Driven AI System Enables Structured GI Procedure Documentation
Documentation during gastrointestinal (GI) procedures often competes with real-time clinical decision-making and imposes a significant cognitive burden on physicians. Manual data entry and post-procedure... Read more
EMR-Based Tool Predicts Graft Failure After Kidney Transplant
Kidney transplantation offers patients with end-stage kidney disease longer survival and better quality of life than dialysis, yet graft failure remains a major challenge. Although a successful transplant... Read more
Printable Molecule-Selective Nanoparticles Enable Mass Production of Wearable Biosensors
The future of medicine is likely to focus on the personalization of healthcare—understanding exactly what an individual requires and delivering the appropriate combination of nutrients, metabolites, and... Read moreBusiness
view channel







